Exploratory Data Analysis(EDA)

dev fest challenges

Dependancies

import warnings
warnings.filterwarnings('ignore')

import numpy as np 
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

import the dataset

data=pd.read_csv('indemnity.csv')
data.head(5)

	age	gender	Body mass index	kids	chronic illness	area	bills
0	19	female	27.900	0	yes	southwest	16884.92400
1	18	male	33.770	1	no	southeast	1725.55230
2	28	male	33.000	3	no	southeast	4449.46200
3	33	male	22.705	0	no	northwest	21984.47061
4	32	male	28.880	0	no	northwest	3866.85520

data.describe()

	age	Body mass index	kids	bills
count	1338.000000	1338.000000	1338.000000	1338.000000
mean	39.207025	30.663397	1.094918	13270.422265
std	14.049960	6.098187	1.205493	12110.011237
min	18.000000	15.960000	0.000000	1121.873900
25%	27.000000	26.296250	0.000000	4740.287150
50%	39.000000	30.400000	1.000000	9382.033000
75%	51.000000	34.693750	2.000000	16639.912515
max	64.000000	53.130000	5.000000	63770.428010

verification if there is null values in the dataset

data.isnull().sum()

age                0
gender             0
Body mass index    0
kids               0
chronic illness    0
area               0
bills              0
dtype: int64

number of person by sex

f,ax=plt.subplots(1,2,figsize=(18,8))
data['gender'].value_counts().plot.pie(explode=[0,0.1],autopct='%1.1f%%',ax=ax[0],shadow=True)

ax[0].set_title('gender')
ax[0].set_ylabel('')

sns.countplot('gender',data=data,ax=ax[1])
ax[1].set_title('gender')

plt.show()

our dataset is balanced for gender

how many person in the dataset grouped by gender and chronic illness ?

data.groupby(['gender','chronic illness'])['chronic illness'].count()

gender  chronic illness
female  no                 547
        yes                115
male    no                 517
        yes                159
Name: chronic illness, dtype: int64

f,ax=plt.subplots(1,2,figsize=(18,8))

data['chronic illness'].value_counts().plot.bar(ax=ax[0])
ax[0].set_title('chronic illness by gender')

sns.countplot('gender',hue='chronic illness',data=data,ax=ax[1])
ax[1].set_title('number of chronic illness by gender')

plt.show()

the variation of bills by the number of kids and gender .

fig = px.histogram(data, x="kids", y="bills", color="gender")
fig.show()

the variation of bills by the body mass index for those who suffer and those who do not suffer from chronic illness.

fig = px.histogram(data, x="Body mass index", y="bills", color="chronic illness")
fig.show()

distribution of body mass index by age

fig = px.density_heatmap(data, x="age", y="Body mass index", marginal_x="rug", marginal_y="histogram")
fig.show()

the variation of body mass index by agefor those who suffer and those who do not suffer from chronic illness.

fig = px.histogram(data, x="age", y="Body mass index", color="chronic illness")
fig.show()

that's all
Thank you for taking the time to read this notebook